School of Geography, University of Leeds, Leeds, United Kingdom.
Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands.
PLoS One. 2019 Jun 26;14(6):e0218324. doi: 10.1371/journal.pone.0218324. eCollection 2019.
A key issue in the analysis of many spatial processes is the choice of an appropriate scale for the analysis. Smaller geographical units are generally preferable for the study of human phenomena because they are less likely to cause heterogeneous groups to be conflated. However, it can be harder to obtain data for small units and small-number problems can frustrate quantitative analysis. This research presents a new approach that can be used to estimate the most appropriate scale at which to aggregate point data to areas.
The proposed method works by creating a number of regular grids with iteratively smaller cell sizes (increasing grid resolution) and estimating the similarity between two realisations of the point pattern at each resolution. The method is applied first to simulated point patterns and then to real publicly available crime data from the city of Vancouver, Canada. The crime types tested are residential burglary, commercial burglary, theft from vehicle and theft of bike.
The results provide evidence for the size of spatial unit that is the most appropriate for the different types of crime studied. Importantly, the results are dependent on both the number of events in the data and the degree of spatial clustering, so a single 'appropriate' scale is not identified. The method is nevertheless useful as a means of better estimating what spatial scale might be appropriate for a particular piece of analysis.
在分析许多空间过程时，一个关键问题是选择适当的分析尺度。对于人类现象的研究，较小的地理单元通常更可取，因为它们不太可能将异质群体混为一谈。然而，对于小单元来说，获取数据可能会更困难，并且小数量的问题可能会使定量分析受阻。本研究提出了一种新方法，可用于估计将点状数据聚合到区域的最合适尺度。
该方法通过创建具有迭代更小单元大小（增加网格分辨率）的多个规则网格，并在每个分辨率下估计点模式两个实现之间的相似性来工作。该方法首先应用于模拟点模式，然后应用于加拿大温哥华市的真实可用犯罪数据。测试的犯罪类型包括住宅盗窃、商业盗窃、车内盗窃和自行车盗窃。
结果为不同类型的犯罪研究提供了最合适的空间单元大小的证据。重要的是，结果取决于数据中的事件数量和空间聚类程度，因此没有确定单一的“合适”尺度。然而，该方法可用作更好地估计特定分析可能合适的空间尺度的一种手段。
